Uncertainty in Artificial Intelligence
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Effects of Treatment on the Treated: Identification and Generalization
Ilya Shpitser, Judea Pearl
Many applications of causal analysis call for assessing, retrospectively, the effect of withholding an action that has in fact been implemented. This counterfactual quantity, sometimes called "effect of treatment on the treated," (ETT) have been used to to evaluate educational programs, critic public policies, and justify individual decision making. In this paper we explore the conditions under which ETT can be estimated from (i.e., identified in) experimental and/or observational studies. We show that, when the action invokes a singleton variable, the conditions for ETT identification have simple characterizations in terms of causal diagrams. We further give a graphical characterization of the conditions under which the effects of multiple treatments on the treated can be identified, as well as ways in which the ETT estimand can be constructed from both interventional and observational distributions.
Keywords: null
Pages: 514-521
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PDF Link: /papers/09/p514-shpitser.pdf
AUTHOR = "Ilya Shpitser and Judea Pearl",
TITLE = "Effects of Treatment on the Treated: Identification and Generalization",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "514--521"

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